


"""simple docstring"""



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


lowercase_							=							subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
lowercase_							=							subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode('utf-8').split()

lowercase_							=							'|'.join(sys.argv[1:])
lowercase_							=							re.compile(rf"""^({joined_dirs}).*?\.py$""")

lowercase_							=							[x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')



"""simple docstring"""



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> Union[str, Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			os.path.join(args.tf_model_dir			,   '''parameters.json'''	)
	lowerCAmelCase_      							=			json.loads(open(_lowercase	).read()	)
	if not params:
		raise ValueError(
		    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
	if not args.output.endswith('''.pt'''	):
		lowerCAmelCase_      							=			args.output + '''.pt'''
	lowerCAmelCase_      							=			OrderedDict()
	with tf.device('''/CPU:0'''	):
		lowerCAmelCase_      							=			tf.train.load_checkpoint(args.tf_model_dir	)
		lowerCAmelCase_      							=			reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			lowerCAmelCase_      							=			reader.get_tensor(_lowercase	).astype(np.floataa	)
			if key_name.endswith('''/adam_m'''	) or key_name.endswith('''/adam_v'''	):
				continue
			if key_name.startswith('''pasts/'''	):
				if key_name.startswith('''pasts/mlp'''	):
					lowerCAmelCase_      							=			int(key_name[9]	)
				elif key_name.startswith('''pasts/out'''	):
					lowerCAmelCase_      							=			8
				lowerCAmelCase_      							=			'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/moe'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/switch_gating/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/softmlp/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/wo/kernel'''	) or key_name.endswith('''/wi/kernel'''	):
					lowerCAmelCase_      							=			key_name[-9:-7]
					for i in range(1_6	):
						lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
						lowerCAmelCase_      							=			(
						    vnp[i].transpose([1, 0]	).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/mlp'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/p1/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p1/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/ln'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/att'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/qkv/kernel'''	):
					lowerCAmelCase_      							=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					lowerCAmelCase_      							=			state[:, 0, :, :]
					lowerCAmelCase_      							=			state[:, 1, :, :]
					lowerCAmelCase_      							=			state[:, 2, :, :]
					lowerCAmelCase_      							=			(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/o/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
					lowerCAmelCase_      							=			(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/an'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif (
			    key_name.startswith('''model/wte'''	)
			    or key_name.startswith('''model/wpe'''	)
			    or key_name.startswith('''model/ete'''	)
			):
				lowerCAmelCase_      							=			{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
				    key_name[-3:]
				]
				lowerCAmelCase_      							=			'''model.%s.weight''' % nlayer
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				if key_name.startswith('''model/wte'''	):
					lowerCAmelCase_      							=			'''lm_head.weight'''
					lowerCAmelCase_      							=			vnp.copy()  # same in embedded
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/wob'''	):
				lowerCAmelCase_      							=			'''final_logits_bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			state.reshape((1, -1)	)
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense/kernel":
				lowerCAmelCase_      							=			'''model.last_project.weight'''
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense_1/bias":
				lowerCAmelCase_      							=			'''model.last_project.bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
	torch.save(_lowercase			,   args.output	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser(
		    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
		parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
		lowercase_							=							parser.parse_args()
		convert_tf_gptsan_to_pt(args)